文章基本信息

标题：Optimizing a multi-echelon supply chain network flow using nonlinear fuzzy multi-objective integer programming: Genetic algorithm approach
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作者：Hessam Zandhessami ; Mehrzad Kashi Zonozi ; Mohammad Ali Afshari 等
期刊名称：Management Science Letters
印刷版ISSN：1923-9335
电子版ISSN：1923-9343
出版年度：2012
卷号：2
期号：6
页码：1871-1884
DOI：10.5267/j.msl.2012.06.036
出版社：Growing Science
摘要：The aim of this paper is to present mathematical models optimizing all materials flows in supply chain. In this research a fuzzy multi-objective nonlinear mixed- integer programming model with piecewise linear membership function is applied to design a multi echelon supply chain network (SCN) by considering total transportation costs and capacities of all echelons with fuzzy objectives. The model that is proposed in this study has 4 fuzzy functions. The first function is minimizing the total transportation costs between all echelons (suppliers, factories, distribution centers (DCs) and customers). The second one is minimizing holding and ordering cost on DCs. The third objective is minimizing the unnecessary and unused capacity of factories and DCs via decreasing variance of transported amounts between echelons. The forth is minimizing the number of total vehicles that ship the materials and products along with SCN. For solving such a problem, as nodes increases in SCN, the traditional method does not have ability to solve large scale problem. So, we applied a Meta heuristic method called Genetic Algorithm. The numerical example is real world applied and compared the results with each other demonstrate the feasibility of applying the proposed model to given problem, and also its advantages are discussed.
关键词：Supply chain management; Supply chain network; Genetic algorithm; Multi echelon; Fuzzy theory